Article(id=1282030032994419012, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282030021623657005, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2025.08.1376, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1743436800000, receivedDateStr=2025-04-01, revisedDate=1748880000000, revisedDateStr=2025-06-03, acceptedDate=null, acceptedDateStr=null, onlineDate=1783589378831, onlineDateStr=2026-07-09, pubDate=1756310400000, pubDateStr=2025-08-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783589378831, onlineIssueDateStr=2026-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783589378831, creator=13701087609, updateTime=1783589378831, updator=13701087609, issue=Issue{id=1282030021623657005, tenantId=1146029695717560320, journalId=1146031787341344770, year='2025', volume='35', issue='8', pageStart='1', pageEnd='276', issueExtLink='null', onlineDate='null', pubDate='1756310400000', pubDateStr='2025-08-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783589376121, creator='13701087609', updateTime=1783589731222, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282031511155228698, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282030021623657005, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282031511155228699, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282030021623657005, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=244, endPage=252, ext={EN=ArticleExt(id=1282030033199939909, articleId=1282030032994419012, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Study on enterprise-led emergency supply reserve system using Bayesian persuasion theory, columnId=1149733268699918866, journalTitle=China Safety Science Journal, columnName=Emergency technology and management, runingTitle=null, highlight=null, articleAbstract=

In order to effectively address the escalating challenges of natural disasters and enhance the efficiency of the enterprise-led emergency supply reserve system, an economic information disclosure mechanism was proposed to boost enterprise participation. Firstly, an enterprise-led emergency supply reserve model was constructed based on an option contract to clarify the intrinsic driving mechanism of disaster occurrence probability on enterprises' reserve decisions. Then, the Stackelberg sequential game framework was further employed to evaluate the effectiveness of government subsidy policies in increasing supply reserve volumes and improving government-enterprise objectives. Furthermore, Bayesian persuasion theory was introduced to design a government information disclosure mechanism, aimed at guiding enterprises' reserve decisions and improving the overall efficiency of relief supply reserves. Finally, a numerical analysis was conducted with flood disasters as a specific application scenario. The research findings indicate that the volume of enterprise-led emergency supply reserves is positively correlated with disaster occurrence probability, reflecting enterprises' sensitivity to disasters. Although government subsidies can increase reserve quantities and reduce government costs, they have limitations in improving enterprises' profits. The information disclosure mechanism designed based on Bayesian persuasion theory outperforms both the baseline model and the subsidy model in terms of reserve quantities, government costs, and enterprises' profits. Through effective disaster information disclosure, the government can enhance the relief efficiency of the enterprise-led emergency supply reserve system.

, authors=Hongrui CHU¹, Bin HUANG², authorsList=Hongrui CHU, Bin HUANG, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1282030035217400136, articleId=1282030032994419012, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于贝叶斯劝说理论的企业代储应急物资研究, columnId=1149733268855108116, journalTitle=中国安全科学学报, columnName=应急技术与管理, runingTitle=null, highlight=null, articleAbstract=

为应对日益频发的自然灾害，保障企业代储应急物资的效率，从经济学信息披露角度给出加强企业参与度的措施。首先，基于期权合同构建企业代储应急物资模型，说明灾害发生概率对企业代储决策的内在驱动作用；其次，进一步采用斯塔克尔伯格序贯博弈框架，评估政府补贴政策在提升物资储备量和政企目标方面的效果；然后，引入贝叶斯劝说理论，设计政府的信息披露机制，引导企业代储决策，并增强应急物资储备的整体效率；最后，以洪涝灾害为具体应用场景，进行数值分析。研究结果表明：企业的应急物资代储量与灾害发生概率呈正相关关系，显示出企业对灾害风险的敏感认知；虽然政府补贴能够增加物资储备量并降低政府成本，但在提升企业收益方面存在局限性；基于贝叶斯劝说理论设计的信息披露机制，在提升物资储备量、降低政府成本和增加企业收益等方面均展现出优于基准模型和补贴模型的显著优势。政府可通过有效的披露灾害信息，改善企业代储应急物资模式的物资救援效率。

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褚宏睿（1987—），男，山东枣庄人，博士，副教授，主要从事应急物流与供应链等方面的研究。E-mail: chuhongrui@cueb.edu.cn。

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褚宏睿（1987—），男，山东枣庄人，博士，副教授，主要从事应急物流与供应链等方面的研究。E-mail: chuhongrui@cueb.edu.cn。

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